A queuing-based decision support methodology to estimate hospital inpatient bed demand

Cochran, Jeffery K.; Roche, Kévin

doi:10.1057/palgrave.jors.2602499

Cited by 50 publications

(31 citation statements)

References 16 publications

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“…Akcali et al [13] developed a nonlinear integer programming formulation and a network flow model that incorporates facility performance and budget constraints to determine optimal hospital bed capacity over a finite planning horizon. Cochran and Roche [14] proposed a capacity planning tool based on queuing theory and financial data to determine the distribution of inpatient beds by adjusting input parameters, including patient volume and mix. Kokangul [15] promoted an integrated method that combines deterministic and stochastic approaches to optimize hospital bed capacity.…”

Section: Literature Reviewmentioning

confidence: 99%

Stochastic resource allocation in emergency departments with a multi-objective simulation optimization algorithm

Feng

Chen

2015

Health Care Manag Sci

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The number of emergency cases or emergency room visits rapidly increases annually, thus leading to an imbalance in supply and demand and to the long-term overcrowding of hospital emergency departments (EDs). However, current solutions to increase medical resources and improve the handling of patient needs are either impractical or infeasible in the Taiwanese environment. Therefore, EDs must optimize resource allocation given limited medical resources to minimize the average length of stay of patients and medical resource waste costs. This study constructs a multi-objective mathematical model for medical resource allocation in EDs in accordance with emergency flow or procedure. The proposed mathematical model is complex and difficult to solve because its performance value is stochastic; furthermore, the model considers both objectives simultaneously. Thus, this study develops a multi-objective simulation optimization algorithm by integrating a non-dominated sorting genetic algorithm II (NSGA II) with multi-objective computing budget allocation (MOCBA) to address the challenges of multi-objective medical resource allocation. NSGA II is used to investigate plausible solutions for medical resource allocation, and MOCBA identifies effective sets of feasible Pareto (non-dominated) medical resource allocation solutions in addition to effectively allocating simulation or computation budgets. The discrete event simulation model of ED flow is inspired by a Taiwan hospital case and is constructed to estimate the expected performance values of each medical allocation solution as obtained through NSGA II. Finally, computational experiments are performed to verify the effectiveness and performance of the integrated NSGA II and MOCBA method, as well as to derive non-dominated medical resource allocation solutions from the algorithms.

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Section: Literature Reviewmentioning

confidence: 99%

Stochastic resource allocation in emergency departments with a multi-objective simulation optimization algorithm

Feng

Chen

2015

Health Care Manag Sci

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“…Cochran & Roche, 2007 use the discipline to estimate demand for inpatient beds at a level 1 trauma facility in the United States. The activities of an intensive care unit are modelled in in which the optimal bed allocation is determined and what-if type scenarios examined.…”

Section: Problems In the Theory Of Probabilities Of Significance In Amentioning

confidence: 99%

Modelling activities at a neurological rehabilitation unit

Griffiths

Williams

Wood

2013

European Journal of Operational Research

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DeclarationThis work has not previously been accepted in substance for any degree and is not concurrently being submitted in candidature for any degree. Signed:Date: Statement 1This thesis is the result of my own investigations, except where otherwise stated. Other sources are acknowledged by giving explicit references. A bibliography is appended. Signed:Date: Statement 2I hereby give consent for my thesis, if accepted, to be available for photocopying and interlibrary loan, and for the title and summary to be made available to outside organisations. SummaryA queuing model is developed for the neurological rehabilitation unit at Rookwood Hospital in Cardiff. Arrivals at the queuing system are represented by patient referrals and service is represented by patient length of stay (typically five months). Since there are often delays to discharge, length of stay is partitioned into two parts: admission until date ready for discharge (modelled by Coxian phase-type distribution) and date ready for discharge until ultimate discharge (modelled by exponential distribution). The attributes of patients (such as age, gender, diagnosis etc) are taken into account since they affect these distributions. A computer program has been developed to solve this multi-server (21 bed) queuing system to produce steady-state probabilities and various performance measures.However, early on in the project it became apparent that the intensity of treatment received by patients has an effect on the time, from admission, until they are ready for discharge. That is, the service rates of the Coxian distribution are dependent on the amount of therapy received over time. This directly relates to the amount of treatment allocated in the weekly timetables. For the physiotherapy department, these take about eight hours to produce each week by hand. In order to ask the valuable what-if questions that relate to treatment intensity, it is therefore necessary to produce an automated scheduling program that replicates the manual assignment of therapy. The quality of timetables produced using this program was, in fact, considerably better than its alternative and so replaced the by-hand approach. Other benefits are more clinical time (since less employee input is required) and a convenient output of data and performance measures that are required for audit purposes.Once the model is constructed a number of relevant hypothetical scenarios are considered.Such as, what if delays to discharge are reduced by 50%? Also, through the scheduling program, the effect of changes to the composition of staff or therapy sessions can be evaluated, for example, what if the number of therapists is increased by one third? The effects of such measures are analysed by studying performance measures (such as throughput and occupancy) and the associated costs.

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“…Researchers have worked on hospital bed management problem for almost 30 years [3], using queuing, simulation and optimization models for capacity planning and allocation of resources [4][5][6][7][8][9]. However, the subject of bed overflow was less investigated.…”

Section: Literature Reviewmentioning

confidence: 99%

Intelligent Analysis of Acute Bed Overflow in a Tertiary Hospital in Singapore

Teow¹,

El‐Darzi

Foo

et al. 2011

J Med Syst

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Hospital beds are a scarce resource and always in need. The beds are often organized by clinical specialties for better patient care. When the Accident & Emergency Department (A&E) admits a patient, there may not be an available bed that matches the requested specialty. The patient may be thus asked to wait at the A&E till a matching bed is available, or assigned a bed from a different specialty, which results in bed overflow. While this allows the patient to have faster access to an inpatient bed and treatment, it creates other problems. For instance, nursing care may be suboptimal and the doctors will need to spend more time to locate the overflow patients. The decision to allocate an overflow bed, or to let the patient wait a bit longer, can be a complicated one. While there can be a policy to guide the bed allocation decision, in reality it depends on clinical calls, current supply and waiting list, projected supply (i.e. planned discharges) and demand. The extent of bed overflow can therefore vary greatly, both in time dimension and across specialties. In this study, we extracted hospital data and used statistical and data mining approaches to identify the patterns behind bed overflow. With this insight, the hospital administration can be better equipped to devise strategies to reduce bed overflow and therefore improve patient care. Computational results show the viability of these intelligent data analysis techniques for understanding and managing the bed overflow problem.

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A queuing-based decision support methodology to estimate hospital inpatient bed demand

Cited by 50 publications

References 16 publications

Stochastic resource allocation in emergency departments with a multi-objective simulation optimization algorithm

Stochastic resource allocation in emergency departments with a multi-objective simulation optimization algorithm

Modelling activities at a neurological rehabilitation unit

Intelligent Analysis of Acute Bed Overflow in a Tertiary Hospital in Singapore

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